**1. Introduction**

Using Computational Fluid Dynamics (CFD) simulation is of great importance in modeling reactive flows because it allows for the prediction and analysis of complex fluid dynamics and chemical reactions in a controlled and cost-effective environment [1]. In fluid reactors, the reaction rate depends on many factors such as concentrations of reactant fluid and product fluid, temperature and turbulence, and these key factors vary with time and space coordinate [2]. Knowing the evolution and distribution of the heat and mass transfer factors is important in reactor design and operating condition optimization. CFD simulations can be used to model and simulate the behavior of reactive flows, such as combustion [3], pyrolysis [4–6], gasification [7] and some other chemical reactions-driven fluid flow [8], and get detailed information on heat and mass transfer inside reactors.

There are many advantages of CFD over other traditional research methods. For example, CFD simulations can predict the behavior of combustion, which can be used to optimize combustion systems and reduce emissions. CFD simulations can also be used to optimize the design of reaction systems, such as gas combustion in turbines, boilers, and engines, pyrolysis or gasification in thermal reactors by predicting the performance of different designs and optimizing the operating conditions for maximum efficiency. Safety analysis can be performed by CFD simulations as well, by predicting the behavior of the system under different conditions and identifying potential risks. A distinct advantage of CFD simulations is that it can provide a costeffective means of analyzing and optimizing reactive flow systems, as they can be used to simulate and analyze the behavior of the system under a wide range of conditions without the need for expensive physical testing [9].

The results of a CFD simulation are obtained through numerical methods that solve the fluid flow governing equations using a finite difference or a finite volume method. The numerical methods used in CFD simulations involve dividing the fluid domain into a grid or mesh of discrete cells and then solving the equations governing fluid flow and heat transfer at each of these grid points or cells. The numerical methods used in CFD simulations are complex and require high-performance computing resources. The accuracy of the results depends on the quality of the mesh, the numerical methods used and the convergence criteria [10]. Therefore, CFD simulations require a deep understanding of numerical methods with careful validation and verification to ensure that the results are accurate and reliable.

To better understand CFD simulations, learning the basics of CFD theory is essential for a few reasons. CFD basic theory provides a fundamental understanding of the governing equations and numerical methods used in CFD simulations. This understanding is essential for developing accurate and reliable CFD simulations. A thorough understanding of CFD theory allows for the development of more efficient and accurate simulations. This is because it enables the user to select appropriate numerical methods, grid sizes and convergence criteria. CFD simulations can be complex, and errors can arise due to various reasons. Understanding CFD theory allows the user to identify the source of the error and develop solutions to fix it. CFD theory is constantly evolving, and researchers are continually developing new methods and algorithms to improve the accuracy and efficiency of CFD simulations. A strong understanding of CFD theory is essential for contributing to the advancement of the field.
